In the field of sediment disaster, an index of how much similar rainfall was experienced in the past is calculated using short rainfall (short-term in several minutes to several hours) and long rainfall (long-term in several days to several weeks), and it is determined whether disaster may occur at the time of rainfall based on the computed index and the past disasters. An example of the index is an output value (an RBFN output value) obtained via a radial basis function network (RBFN) which is a neural network.
There is a known technique in the art for computing the probability of occurrence of disaster by obtaining a function for computing the probability of occurrence of disaster from the RBFN output value by a regression analysis and reading the obtained function and observation data obtained for each disaster occurrence factor. Examples include Japanese Laid-open Patent Publication No. 2004-003274, No. 2010-197185, No. 2010-271877, No. 2004-346653, and No. 2015-232537.
Sediment disaster is predicted based on, for example, the accumulated rainfall up to the present and past observation data. However, an increase in accumulated rainfall due to unexpected continuing rainfall and unexpected immediately following heavy rain may cause the accumulated rainfall to exceed a disaster critical line (CL). For this reason, the probability of exceeding the CL after a unit time (hereinafter referred to as “excess probability”) may be obtained in advance.
The above techniques take no account of the probability of unexpected continuing rainfall and unexpected immediately following heavy rain, so that the same RBFN value indicates the same disaster occurrence probability, that is, the same excess probability.
In one aspect, the influence of a precipitation zone is appropriately reflected to the probability of exceeding a critical line on the occurrence of disaster.